Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Surface protein imputation from single cell transcriptomes by deep neural networks

Zilu Zhou, Chengzhong Ye, Jingshu Wang, Nancy R. Zhang
doi: https://doi.org/10.1101/671180
Zilu Zhou
1Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA
2Department of Statistics, University of Pennsylvania, Philadelphia, PA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Chengzhong Ye
3School of Medicine, Tsinghua University, Beijing, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jingshu Wang
2Department of Statistics, University of Pennsylvania, Philadelphia, PA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nancy R. Zhang
2Department of Statistics, University of Pennsylvania, Philadelphia, PA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: nzh@wharton.upenn.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Data/Code
  • Preview PDF
Loading

Abstract

While single cell RNA sequencing (scRNA-seq) is invaluable for studying cell populations, cell-surface proteins are often integral markers of cellular function and serve as primary targets for therapeutic intervention. Here we propose a transfer learning framework, single cell Transcriptome to Protein prediction with deep neural network (cTP-net), to impute surface protein abundances from scRNA-seq data by learning from existing single-cell multi-omic resources.

Footnotes

  • https://github.com/zhouzilu/cTPnet

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted June 14, 2019.
Download PDF

Supplementary Material

Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Surface protein imputation from single cell transcriptomes by deep neural networks
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Surface protein imputation from single cell transcriptomes by deep neural networks
Zilu Zhou, Chengzhong Ye, Jingshu Wang, Nancy R. Zhang
bioRxiv 671180; doi: https://doi.org/10.1101/671180
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Surface protein imputation from single cell transcriptomes by deep neural networks
Zilu Zhou, Chengzhong Ye, Jingshu Wang, Nancy R. Zhang
bioRxiv 671180; doi: https://doi.org/10.1101/671180

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4119)
  • Biochemistry (8828)
  • Bioengineering (6532)
  • Bioinformatics (23484)
  • Biophysics (11805)
  • Cancer Biology (9223)
  • Cell Biology (13336)
  • Clinical Trials (138)
  • Developmental Biology (7442)
  • Ecology (11425)
  • Epidemiology (2066)
  • Evolutionary Biology (15173)
  • Genetics (10453)
  • Genomics (14056)
  • Immunology (9187)
  • Microbiology (22199)
  • Molecular Biology (8823)
  • Neuroscience (47626)
  • Paleontology (351)
  • Pathology (1431)
  • Pharmacology and Toxicology (2493)
  • Physiology (3736)
  • Plant Biology (8090)
  • Scientific Communication and Education (1438)
  • Synthetic Biology (2224)
  • Systems Biology (6042)
  • Zoology (1254)